Senior System Software Engineer, Interactive World Models

Nvidia

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $184,000–$287,500 / yr
Posted: 52 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $193k

This role $236k

$135k most similar roles pay here $304k

This role pays more than 87% of similar roles. Most pay $151,000–$235,750 — the shaded band above. At the midpoint, this role pays about $236k versus about $193k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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At a glance

TL;DR · Senior System Software Engineer, Interactive World Models

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As a Senior System Software Engineer, Interactive World Models, you will join the FlashDreams team to bridge the gap between research and systems engineering. You will design, build, and improve production-quality systems for world models, simulation, rendering, and real-time inference from prototype through release. Your daily work involves connecting generated worlds, reconstructed scenes, controllable simulation states, and evaluation loops into cohesive end-to-end systems while optimizing performance, quality, scalability, and reliability on NVIDIA GPUs. You will collaborate with research, simulation, rendering, robotics, and autonomous-vehicle teams to manage technical tradeoffs and own complex modules. The role requires expertise in CUDA, PyTorch, and modern ML frameworks. Relevant experience includes generative video, diffusion or flow-matching models, neural rendering, Gaussian splatting, NeRFs, and large-scale ML systems to solve problems in robotics, autonomous vehicles, and medical training.

What does a System Software Engineer earn in California?

Median $235750 from 37 postings across 4 companies.

See salary data

Skills

CUDA PyTorch GPU Optimization Computer Vision Neural Rendering NeRFs Gaussian Splatting Simulation Real-time Inference Distributed Training synthetic-data generation Robotics Autonomous Vehicles

What you'll do

Design and build production-quality systems for world models, simulation, rendering, and real-time inference.
Integrate generated worlds, reconstructed scenes, and controllable simulation states into cohesive end-to-end systems.
Optimize system performance, quality, scalability, and reliability specifically on NVIDIA GPUs.
Transition complex research prototypes into reliable products through technical trade-offs and testing.
Develop and optimize GPU-accelerated software using CUDA and modern ML frameworks like PyTorch.
Own complex modules or cross-team projects that shape the technical direction and product roadmaps.
Integrate capabilities from various domains into downstream workflows for robotics, autonomous vehicles, and gaming.

What we're looking for

BS, MS, or PhD in Computer Science, Electrical Engineering, Physics, or a related field, or equivalent experience.
8+ years of relevant software engineering experience.
Experience in areas such as world models, generative video, neural rendering, simulation, robotics, autonomous vehicles, or large-scale ML systems.
Experience building and optimizing GPU-accelerated software with CUDA and modern ML frameworks like PyTorch.
Demonstrated ability to turn complex prototypes into reliable products through testing and technical trade-offs.
Proven ownership of complex modules or cross-team projects with clear communication skills.
Preferred experience in real-time inference, distributed training, performance profiling, or synthetic data generation.
Optional: Contributions to open-source, publications, patents, or demonstrated leadership in relevant fields.